A deep learning approach to pupillometry

Abstract

The Pupillary Light Reflex (PLR) refers to the change in pupil size due to changes in illumination. The PLR is used by clinicians for the non-invasive assessment of the pupillary pathway. Typically, Infrared (IR) illumination based pupillometers are used to measure the PLR. Researchers have explored the problem of robust pupil detection and reconstruction with algorithms based on traditional computer vision techniques. These techniques do not generalize well when tested with visible light (VL) images. The current study presents a novel approach to pupillometry that uses deeplearning (DL) methodology which is applied to VL images. We used public iris datasets (e.g., UBIRISv2) and data augmentation techniques to train our models for robustness. Noise in the images can be due to different lighting conditions, iris colors, pupil shapes, etc. Ellipses were fit to the pupil images and the parameters were extracted. We evaluated a UNet model and its quantized version. A. non-deep learning model (PuRe) was also evaluated. This study also reports the accuracy of these models with real-world experimental data. This work is the first step toward a VL smartphone-based pupillometer that is fast, accurate, and relies on on-device computing. Such a device can be useful in areas where internet access is unavailable and, more importantly, can be used in the field by paramedics for telemedicine purposes.